How Big is the Wealth Effect? Decomposing the

Response of Consumption to House Prices∗

S. Boragan Aruoba

University of Maryland

Ronel Elul

FRB Philadelphia

Sebnem Kalemli-Ozcan

University of Maryland

March 2018

Abstract

We investigate the effect of declining house prices on household consumption be-

havior during 2006–2009. We use an individual-level data set that has detailed infor-

mation on borrower characteristics, mortgages and credit risk. Proxying consumption

by individual-level auto loan originations, we decompose the effect of declining house

prices on consumption into three main channels: wealth effect, household financial con-

straints, and bank health. We find a negligible wealth effect. Tightening household-

level financial constraints can explain 40-45 percent of the response of consumption

to declining house prices. Deteriorating bank health leads to reduced credit supply

both to households and firms. Our dataset allows us to estimate the effect of this on

households as 20-25 percent of the consumption response. The remaining 35 percent

is a general equilibrium effect that works via a decline in employment as a result of

either lower credit supply to firms or the feedback from lower consumer demand. Our

estimate of a negligible wealth effect is robust to accounting for the endogeneity of

house prices and unemployment. The contribution of tightening household financial

constraints goes down to 35 percent, whereas declining bank credit supply to house-

holds captures about half of the overall consumption response, once we account for

endogeneity.

JEL CLASSIFICATION: E32, O16.

KEY WORDS: financial crisis, mortgage, individual-level data, general equilibrium,

bank health, credit supply

∗Correspondence: Aruoba and Kalemli-Ozcan: Department of Economics, University of Maryland, Col-lege Park, MD 20742. Email: aruoba@econ.umd.edu, kalemli@econ.umd.edu. Elul: Research Department,Federal Reserve Bank of Philadelphia, Philadelphia, PA 19106. Email: ronel.elul@phil.frb.org. The authorsthank participants at seminars at University of Maryland, the Federal Reserve Bank of Philadelphia andthe HULM 2017 Conference in Philadelphia for helpful comments, John Chao for useful discussions and DiWang for excellent research assistance. The views expressed in this paper are those of the authors and donot necessarily reflect those of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.

1 Introduction

The U.S. economy experienced a large financial crisis together with a housing bust in 2007–

2008. A deep recession with significant declines of consumption, investment, and employment

has followed. Although there is an extensive theoretical and empirical literature on the causes

and consequences of the crisis, so far there is still no consensus on the role of the channels

linking the housing bust to the recession.

There have been three main narratives of the crisis put forth in the literature. The

first narrative is a wealth shock to consumers via a decline in their housing wealth, which

lead them to cut their consumption, and this in turn lead to a decline in output. Mian

and Sufi (2009), Mian and Sufi (2011), and Mian, Rao, and Sufi (2013) have been the main

proponents of this view, where an increase in household leverage predict the subsequent crisis,

de-leveraging and consumption decline. They show empirically a strong relationship between

these variables and argue that the recession is due to this demand channel via declining

consumption.1 The second narrative is about households being financially constrained as a

result of a shock to their housing wealth. When house values go down, the value of housing

collateral falls and households’ borrowing constraints get tighter, which in turn might prevent

them from borrowing. Berger, Guerrieri, Lorenzoni, and Vavra (2015) and Kaplan, Mitman,

and Violante (2016) have proposed models where this channel is important for the decline

in consumption and the associated recession. Aladangady (2017) provides empirical support

for this channel, where a large part of the response of consumption to changing house values

are driven by credit-constrained households. The final narrative is about the shocks to

the financial sector which tighten their financial constraints, and they in turn reduce credit

supply to both households and firms. Households decrease consumption as a result, and

firms cut down employment and investment. There is an extensive empirical debate on the

effect of reduced credit supply on firm employment. While Duygan-Bump, Levkov, and

Montoriol-Garriga (2015) and Greenstone, Mas, and Nguyen (2015) find that reduced credit

supply can only account for less than one-tenth of the decline in employment, Chodorow-

Reich (2014), Chen, Hanson, and Stein (2017) and Gilchrist, Siemer, and Zakrajek (2017)

find that up to one-third of the employment decline may be driven by bank shocks.

Our goal in this paper is to quantify each of these narratives using detailed individual-level

data, which include mortgage and credit risk information. We know from the existing lite-

rature that shocks to house values create a large consumption response at an aggregate (ZIP

1Philippon and Midrigan (2016), using aggregate data and a model, argue that household de-leveragingby itself cannot explain a large part of decline in employment and output.

1

Figure 1: House Prices and Consumption: Channels

House Prices ↓ (Exogenous)

Consumers

HouseholdCredit Supply↓

HouseholdWealth

HouseholdFinancial

Constraint

Banks

BankHealth

Firms

FirmCredit Supply↓

Firm Wealth &Financial

Constraints

Consumption ↓

LocalDemand ↓

Employment ↓General Equilibrium

Feedback

1

code or county) level, but we have only scant evidence about the channel(s) such a response

operates through. The key shortcoming in most of the literature so far is the unavailability

of individual-level consumption data and the inability to combine various individual-level

controls in conjunction with more aggregate controls to identify these channels. It is not

possible, for example, to know who is credit-constrained and where households are in their

life cycle, which directly affects their housing demand, without individual-level data.

Figure 1 shows all of the possible channels that will lead to lower consumption as a

result of an exogenous decline in house prices, where we show three players in the same

locality: consumers, banks and firms. First, on the household side, we argue that as a

result of declining house prices, there will be both a wealth effect, denoted with the arrow

“household wealth” and a collateral shock effect, denoted with the arrow “household financial

constraints.” Although there are models that combine these effects under a single wealth

effect,2 we argue that their effects have to be quantified separately. Why this is important?

In the standard permanent income model, a shock to housing wealth will have no effect on

consumption since positive endowment effects will be canceled out by negative cost of living

effects, as shown by Buiter (2008). In the context of the life-cycle model, if homeowners are

2See for example Kaplan, Mitman, and Violante (2016).

2

likely to sell their house in the future, there can be positive wealth effects via rising house

prices as modeled by Sinai and Souleles (2005). In terms of the current debate, many theory

papers argue that, to be able to match the large responses of consumption to house prices

changes found in the data by Mian, Rao, and Sufi (2013), one needs collateralized lending

that amplifies the impact of housing wealth on consumption.3 Our individual-level data and

methodology will allow us to separate these effects.

Next, as shown in the figure, there is the effect of house price declines on bank health.

If banks are exposed to the real estate market, housing price declines constitute a negative

balance sheet shock to banks, which results in banks cutting credit supply both to households

and firms. As argued by Justiniano, Primiceri, and Tambalotti (2017) an increase in credit

supply is the only force that can match the empirical regularities in the boom period. They

argue that looser borrowing constraints cannot account all for the facts since they only shift

the demand for credit. In their model these forces interact, a lending constraint on the bank

side and a household borrowing constraint are both in play during the boom-bust phase.

They argue that in the models without an exogenous credit supply decline, tightening of the

household borrowing constraint put upward pressure on interest rates, which has not been

observed during the boom phase. Hence, we believe it is important to quantify this effect

separately than the previous ones.4

Lower credit supply to firms will lead to lower employment and investment. As argued

above there is a debate in the empirical literature on the size of this effect. Another possible

channel is, as shown by the dotted arrow, a collateral shock to firms’ balance sheet if firms’

owners use their own housing wealth as collateral to get loans to invest and to produce.5 We

will not be able to study this channel, since we do not have information on firms’ or their

owners’ real estate wealth. In addition, due to low consumption, demand for firms’ output

will be lower, which will also lead firms to decrease employment, as shown by the “local

demand” arrow following the work of Mian and Sufi as cited above. Any firm-level response

via lower employment will feed back to lower consumption due to general equilibrium, as

shown with the bottom arrow. We will be able to identify these effects collectively using

3See Berger, Guerrieri, Lorenzoni, and Vavra (2015), Guerrieri and Iacoviello (2017), Iacoviello (2005).More generally (outside housing), see Barro (1976), Stiglitz and Weiss (1981), Hart and Moore (1994),Kiyotaki and Moore (1997), Bernanke, Gertler, and Gilchrist (1999).

4Gropp, Krainer, and Laderman (2014), show empirically that renters with low risk scores, comparedto homeowners in the same markets, reduced their levels of debt more in counties where house prices fellmore. This suggests that the observed reductions in aggregate borrowing were more driven by cutbacks inthe provision of credit than by a demand-based response to lower housing wealth.

5See Decker (2015) who shows in a model that this channel is important for the decline in start-upactivity. See Bahaj, Foulis, and Pinter (2017) for an empirical study of this channel for U.K.

3

county-level employment.

Not only the literature that studies the Great Recession, but also the broad literature

that tries to understand the effect of house prices and housing wealth on consumption takes

by and large an aggregate approach. The early literature uses time series data from the U.S.

as a whole, and the later literature uses geographic variation across states or counties. In

either case, aggregate time-series and cross-sectional correlations make identification hard.

For example, expectations about future income, can drive both consumption patterns and

house prices. As shown by Attanasio, Blow, Hamilton, and Leicester (2009) and Calomi-

ris, Longhofer, and Miles (2009), the strong aggregate relation between house prices and

consumption shown by Case, Quigley, and Shiller (2005), Carroll and Kimball (1996), and

Carroll, Otsuka, and Slacalek (2011) goes away once expectations of income and other com-

mon factors are controlled. Attanasio, Blow, Hamilton, and Leicester (2009) is an early

paper that shows similar responses from renters and home owners, which again indicates

the existence of common factors in aggregate data. Demyanyk, Hryshko, Luengo-Prado, and

Sørensen (2015) also show that unemployment, income, and debt are important determinants

of consumption in the aggregate data.

In the aggregate data, there can also be an omitted variable problem related to com-

positional changes in the population, such as the effect of age on housing demand. Both

Calomiris, Longhofer, and Miles (2012) and Campbell and Cocco (2007) show that age pro-

file is very important for the relation between housing wealth and consumption where older

cohorts have larger response.6 In the context of the Great Recession, two set of authors

challenged findings of Mian and Sufi also based on compositional effects. Adelino, Schoar,

and Severino (2017) and Albanesi, De Giorgi, and Nosal (2017) argue that credit growth

between 2001 and 2007 was concentrated in the prime segment, debt to high risk borrowers

was virtually constant for all debt categories during this period, and default among high

income prime borrowers were common during the post period.7 They argue that results of

Mian and Sufi confound life-cycle debt demand of borrowers who were young at the start of

the boom, with an expansion in credit supply over that period.

Our unique data set will help us to solve this identification problem caused by using

aggregate data, and help us to identify the channels outlined above in Figure 1. We use

individual-level data from two sources that gives us most detail to-date in terms of individual

6See also Charles, Hurst, and Notowidigdo (forthcoming).7Albanesi, De Giorgi, and Nosal (2017) also use individual-level data from one of the datasets we use,

Federal Reserve Bank of New York/Equifax Consumer Credit Panel, but focus on growth in mortgage debtprior to crisis and subsequent defaults rather than consumption response as we do.

4

mortgages and Equifax Risk Scores. Our first dataset is the Federal Reserve Bank of New

York/Equifax Consumer Credit Panel (CCP), a quarterly database of consumer credit bureau

records for a random 5 percent sample of consumers with a credit bureau record. Our second

dataset is a match between credit bureau data with more detailed information on residential

first mortgages from loan servicing data. This matched dataset is Equifax Credit Risk Insight

Servicing (Equifax Credit) and McDash Analytics, LLC, a wholly owned subsidiary of Black

Knight Financial Services, LLC. (McDash) known as CRISM. We then restrict attention to

those borrowers who can be found in the CCP. As a result we have a random representative

sample of borrower-level information on all loans of the borrower, including any auto loans,

borrower’s Equifax Risk Score, borrower’s age and detailed characteristics of the borrower’s

mortgages, most notably the appraised value of the property, and the type of mortgage.

As a proxy for consumption we use a binary variable at the individual level that represents

origination of an auto loan in 2009. This resembles the ZIP code level new car registration

data that Mian, Rao, and Sufi (2013) use in their analysis, and it has certain advantages,

which we discuss in detail. The most important advantage is that it is at the individual

level. Using an individual-level measure of consumption, we are able to see how a decline

in housing wealth affects consumption, once we control for various aggregate variables. To

further dissect the effects, we are also able to focus on various subgroups in the population

based on their borrower characteristics.

In addition to changes in house prices, we have five main controls: first and foremost,

the life-cycle age profile is controlled at the individual level by age and age square terms.

Then, we include controls for ZIP code level car sales in 2006, change in the county-level

unemployment rate between 2006 and 2009, and a measure of county-level bank health. The

first variable among these aggregate controls is useful to capture preexisting differences across

ZIP codes in consumption (auto purchase) behavior. We obtain this variable by aggregating

our individual-level auto loan origination variable. The second variable is a key measure

for capturing the general equilibrium effect in Figure 1. Finally, bank health, which we

construct using one of Chodorow-Reich (2014)’s bank-level measures, distributed to counties

using banks’ branch shares in the county, is used to control for a county-wide decline in

availability of bank credit. By using the richness of our dataset in terms of information

on borrower characteristics, we interact these control variables with a number of categories,

which may be as detailed as homeowners with a high Equifax Risk Score, who have a fixed-

rate first mortgage, no second mortgage and a loan-to-value (LTV) ratio less than 50%, as

an example.

5

Our results are as follows. Using both datasets, we identify the effect of the combined

household wealth and financial constraints channel as accounting for 40-45% of the overall

consumption response to house prices. The contribution of the decline in credit supply

to households is estimated to be 20-25%. The rest, roughly 35%, as shown in Figure 1,

is a general equilibrium effect that combines the feedback through reduced consumption,

as well as the direct effect of the decline in credit supply to firms. In order to measure

further the contribution of a wealth effect, we focus our analysis on a very specific group of

consumers, which we can identify thanks to the detailed information we have in our data.

These consumers have high credit Equifax Risk Scores, they own their houses outright or

“free and clear” and have not moved between 2006 and 2009. Due to these characteristics,

especially the absence of a mortgage, we expect that the only reason these consumers react

to a decline in house prices will be due to a wealth effect. We demonstrate that, once other

aggregate controls are introduced, these consumers do not react to house prices, indicating

that wealth effect is negligible. This leads us to conclude that the 40-45% contribution we

referred to above is solely due to households’ financial constraints.

We also consider an instrumental variables (IV) strategy to account for the endogeneity

of house prices and unemployment, as well as a possible omitted variable bias. We follow

Aladangady (2017), Gyourko, Saiz, and Summers (2008), and Saiz (2010) to construct our

instruments for house prices. As in those papers, we exploit the variation in lower land

availability and tighter land use regulations that create differences in house prices across

counties. We also construct a Bartik-type instrument following Keys, Tobacman, and Wang

(2014) for employment changes. Our results regarding a negligible wealth effect continues

to hold in our IV specification. The contribution of household financial constraints decline

slightly to 35%, while the contribution of the decline in bank credit supply to households

increase to roughly 50%. This is intuitive since the existence of constrained households

and change in house prices in a given locality can be simultaneously determined by other

characteristics of the locality, which will be controlled once house prices are instrumented

for. Hence the role of exogenous-to-household bank credit supply effect increases.

Using information on mortgage characteristics further, we are also able to describe the

possible reasons why financial constraints affect consumers. We distinguish between ex-ante

and ex-post credit constraints. Ex-ante constraints are those that were in place in 2006,

before the house prices declined, while the ex-post constraints arise, as we demonstrate,

mostly due to the decline in house prices between 2006 and 2009. We show that segments

of the population that are most affected by ex-ante credit constraints, such as those that

6

do not have high Equifax Risk Scores, have large LTVs, those that have adjustable-rate

first mortgages, those that have closed-end second mortgages, or a combination of these

characteristics, respond much stronger to change in house prices. These responses areup to

an order of magnitude larger than those of much less constrained groups mentioned above.

Taking into account both the response of these constrained groups and their population

weights, at least 70% of the consumption response due to financial constraints are as a result

of ex-ante constraints. Regarding ex-post credit constraints, we show that the decline in

house prices is a strong predictor of whether or not the Equifax Risk Score of a consumer

falls in 2009, especially for those who were borrowers with a high Equifax Risk Score and

a moderate-to-large LTV in 2006. We argue that this is because these borrowers default or

fall behind on their mortgages, which reduce their Equifax Risk Scores. This, in turn, means

that they have difficulty in getting a loan to purchase a car, which leads to the reduction in

their consumption.

The closest paper to our work is by Aladangady (2017). To the best of our knowledge this

is the only other paper using individual-level data to investigate the consumption response to

change in house prices. He finds results similar to ours in terms of importance of household

level financial constraints. There are two main differences between our paper and his. First,

we can account for general equilibrium effects and the effect of bank health. Second, we

have a much larger and detailed individual-level data that help us identify both ex-ante

and ex-post borrowing constraints. His key variables to identify constrained households are

refinancing, household leverage and debt service, whereas we have direct data on loan types

and individuals’s credit risk. His results point to the key role played by financial constraints,

whereas our results give an equal role to these constraints and bank health once endogeneity

are accounted for.

We proceed as follows. Section 2 discusses the data in detail. Section 3 presents our

econometric methodology including the IV analysis. Section 4 presents the results and

Section 5 concludes.

2 Data

This section introduces our individual-level data in detail. We will go over the sources of

data first and then explain how we construct our variables and show descriptive statistics.

7

Figure 2: Share in Census vs. Share in CCP (Counties)

Notes: Each dot is a county. Census share and CCP share are on the x-axis and y-axis,respectively. The line shown is the regression line.

2.1 Data Sources

Our main dataset is the Federal Reserve Bank of New York/Equifax Consumer Credit Panel

(CCP), a quarterly database of consumer credit bureau records for a random 5 percent

sample of consumers with a bureau record. We restrict attention to primary CCP consumers.

Available data fields include total balances and aggregate delinquency status on a variety of

consumer credit obligations such as mortgages, auto loans and credit cards, the proprietary

Equifax Risk Score, as well as some loan-level information on first and second mortgages. We

are also able to calculate the age of the consumers based on the birth year that is provided in

CCP. As can be seen from Figure 2, which shows the share of a county within total US census

population versus the share of that county within total CCP, this dataset is representative

of the broader population.

For a sample of these borrowers we have a match between their credit bureau file and

more detailed information on their residential first mortgage. This matched dataset is known

as CRISM.8 This dataset is constructed by taking mortgages originated in the McDash

8See Elul and Tilson (2015) for more details on the CRISM dataset. The exact details of the matchingprocedure are proprietary, but it is an anonymous match, using loan amount and other loan characteristics,and is similar to that in Elul, Souleles, Chomsisengphet, Glennon, and Hunt (2010).

8

dataset and matching them to the primary borrowers Equifax Credit file. The McDash

dataset, which forms the starting point for CRISM, captures approximately two thirds of

all mortgage originations during this time period. The CRISM database begins in June

2005 and we restrict attention to consumers who had a first lien as of December 2006. The

matched data gives more detailed information on the borrower’s mortgages, most notably the

appraised value of the property (which allows us to calculate a loan-to-value ratio), interest

rate, other characteristic such as whether it is fixed or adjustable rate, low documentation,

etc., and monthly mortgage performance information. We further restrict attention to those

borrowers who appear in CCP (recall that this is a random 5% sample), so that we have a

full panel of credit bureau variables for them.

2.2 Defining Groups of Individuals in CCP and in CRISM

Our base CCP dataset consists of 6.5 million consumers, who are in the sample in both

2006Q4 and 2009Q4, and who have an address in the same ZIP code at the start and end of

the sample period. This ensures that they are all exposed to the same local aggregate house

price shock. In order to correctly decompose the effect of house price changes, we classify

consumers according to their homeownership status in CCP, as follows:

1. Renters are those who are age 55 or less in 2009, and who had no mortgages in the

CCP dataset from 1999 (its inception) through 2009.

2. Non-mover mortgage-holding homeowners had a mortgage in both 2006Q4 and 2009Q4,

and the same address in both quarters as well.

3. Free-and-clear homeowners had no mortgages in 2006Q4 or 2009Q4, but a mortgage

at some point prior to 2006Q4, and the same address in both 2006Q4 and 2009Q4.

4. Moving homeowners are those with a mortgage in both endpoints but whose address

changed in the interim.9

5. Miscellaneous are those who do not fit in any of the categories above (this includes

borrowers with no mortgage, who are too old to be classified as renters, or those who

do not have a mortgage in one of the end points.)

9For the three homeowner categories described so far, we also require the mortgage to remain in goodstanding between 2002 and 2009.

9

Table 1: Distribution of Characteristics

(a) CCP

Homeownership Status Prime Non-Prime Total

Renters 5.5% 17.3% 22.8%Free-and-Clear Homeowners 6.3% 4.2% 10.4%Non-Mover Homeowners 25.5% 8.8% 34.3%Moving Homeowners 1.6% 0.8% 2.4%Miscellaneous 19.3% 10.8% 30.1%Total 58.2% 41.8% 100.0%

(b) CRISM - 1

LTV Category Prime Non-Prime Total

LTV0 43.1% 11.3% 54.3%LTV1 22.7% 9.8% 32.5%LTV2 9.1% 4.0% 13.2%Total 74.9% 25.1% 100.0%

(c) CRISM - 2

Prime Non-PrimeMortgage Category LTV0 LTV1 LTV2 LTV0 LTV1 LTV2 Total

Fixed Rate 23.2% 10.9% 4.0% 6.5% 5.3% 2.0% 51.9%ARM < 5yr 1.2% 0.9% 0.5% 0.9% 1.2% 0.6% 5.2%ARM ≥ 5yr 1.4% 1.4% 0.7% 0.3% 0.4% 0.2% 4.3%CE Second 3.0% 2.2% 0.8% 1.3% 1.3% 0.5% 9.1%HELOC 14.3% 7.3% 3.1% 2.3% 1.7% 0.7% 29.5%Total 43.1% 22.7% 9.1% 11.3% 9.8% 4.0% 100.0%

The first panel in Table in 1 shows the share of different types of individuals in the

data. Renters make up 23% of the sample, free-and-clear homeowners 10%, non-moving

homeowners 34%, moving homeowners 2%, and miscellaneous 30%. Our analysis will focus

mostly on the first three groups since we can clearly identify their types and argue that they

constitute fairly uniform groups. The other two groups, especially the last one is one with a

great deal of heterogeneity that is hard to disentangle.

For the CRISM dataset, we similarly restrict attention to borrowers who have the same

10

ZIP code in their address in 2006Q4 and 2009Q4. They must also have a first mortgage in

both endpoints as well (although not necessarily the same one.) Our sample size is approx-

imately 650,000 borrowers. For each homeowner in our sample, we compute an estimate of

their updated first-lien loan-to-value (LTV) ratio by taking their McDash mortgage balance

from December 2006, and updating the appraised value from the time of origination to De-

cember 2006. We then categorize CRISM consumers based on this updated first-lien LTV,

dropping observations with updated LTV greater than 125%:

1. LTV0, less than or equal to 50%

2. LTV1 above 50% and less than or equal to 80%

3. LTV2 above 80% and less than or equal to 125%.

These groups roughly represent low, moderate and high levels of LTV. From second panel

of Table 1 we see that 54% of consumers fall in the lowest category, 33% in the moderate

group, and 13% in the high LTV group.

We further classify borrowers in order to analyze the effect of house price changes on

consumption, based on information on the borrowers’ mortgages at the end of 2006, thanks

to the detailed information coming through CRISM. We define five categories and the third

panel of Table 1 reports the shares of these categories in our sample. First, for borrowers

who do not have a second mortgage in December 2006, we break them up into three groups,

based on information from McDash on their first lien:

1. fixed-rate first lien (52% of total sample),

2. adjustable-rate mortgage (ARM) with a fixed period of less than five years (5%),

3. ARM with a fixed period of five years or more (4%).

Then for borrowers with a second lien, we construct two additional categories, depending

on the second mortgage type, dropping 1.8% of our sample who have both types of second

liens:

1. closed-end second (9%),

2. home equity line of credit (HELOC) (30%).

An important piece of data we have, which helps distinguish our work from some of the

recent literature, is the Equifax Risk Score of the individual on a quarterly basis. Instead of

11

Figure 3: Fraction of Non-prime Borrowers Across ZIP Codes

using this score directly in our analysis, we create two groups based on Equifax Risk Scores.

We define a consumer as:

1. non-prime if he has an Equifax Risk Score of below 70010;

2. prime those with Equifax Risk Scores of 700 or higher are denoted as prime borrowers.

The prime share in the CCP dataset is 58%. It is 75% in CRISM, which higher since ho-

meowners (CRISM by definition is exclusively composed of homeowners) have higher Equifax

Risk Scores. Figure 3 shows the distribution of ZIP codes with respect to the fraction of

non-prime borrowers. This shows that a vast majority of ZIP codes have a mixture of prime

and non-prime borrowers, and thus ZIP code level variables and the individual-level indi-

cator of prime status will contain largely independent information. Table 1 also shows the

breakdown of each of the other categories we defined above with respect to prime status.

While not central to our analysis, there are some interesting observations such as renters

being predominantly non-prime or non-mover homeowners being predominantly prime.

10This is a relatively high score cutoff for nonprime, and it reflects the fact that our analysis focuses onhomeowners, who tend to have higher Equifax Risk Scores.

12

Figure 4: CEX vs. CCP: Fraction of Consumers with an Auto Loan Origination

.08

.09

.10

.11

.12

.13

.14

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15

CCPCEX

Fraction of Consumers with a Auto Loan Origination

2.3 Construction of Consumption Proxy

We proxy for consumption by computing auto loan originations. As the credit bureau dataset

does not give information on individual auto loans, we impute originations by tracking

changes in total balances. For a given consumer in a particular quarter in the credit bureau

dataset, we identify an auto loan origination by an increase in total auto loan balances of

at least $1,000, relative to the previous quarter.11 This procedure tracks the incidence of

auto loan originations in other sources very well: for example, we find that 10.1% of all

consumers have an auto loan origination in 2008 in the CCP, whereas from the Panel Study

of Income Dynamics (PSID) the origination rate in that year is 10.8%.12 We also find that

it matches the share of auto loans originated in the Consumer Expenditure Survey (CEX)

for the period of our analysis (2006-2009) as we show in Figure 4.

By contrast, Mian, Rao, and Sufi (2013) use new auto registrations from Polk, at the

ZIP code level.13 Compared to our measure, the advantage of theirs is that they are able

to capture cash purchases that do not involve any financing. However, Johnson, Pence, and

Vine (2017) report that about 70 percent of household purchases of new vehicles and 35

percent of household purchases of used vehicles are financed with auto loans. In addition,

11Our analysis is robust to different definitions of originations.12This is computed from the 2009 wave of the PSID, using the number of respondents with a vehicle that

was acquired in 2008, and the share of these which were acquired using a loan or lease.13Kaplan, Mitman, and Violante (2017) replicated all the results of Mian, Rao, and Sufi (2013) using

publicly available aggregate data.

13

Johnson, Pence, and Vine (2017) find some additional cyclicality in loan originations, as

compared to auto sales, where in bad times consumers substitute away from new cars into

used cars and they are more likely to use a loan to do so. Using loan originations brings

with it the following advantages. First, we are able to capture both new as well as used

car sales, whereas Mian, Rao, and Sufi (2013) only have new car registrations available to

them. This is an important feature of our analysis, as new vehicles make up only 38% of

all consumer auto purchases.14 In addition, we are able to focus our analysis on household

purchases, whereas the measure used by Mian, Rao, and Sufi (2013) also include business

purchases. Finally, the most important advantage of our data is that it is at the individual

level and sdince it is obtained from credit bureau data, we are also able to exploit other

individual-level characteristics found in our datasets, rather than basing our analysis solely

on aggregate measures.15

2.4 Descriptive Statistics

In Table 2 we show summary statistics of our consumption proxy, as well as four aggregate

control variables we use in our analysis. For 2009, the probability that an individual origi-

nated an auto loan is 8.56% for the CCP overall, and 13.90% for the CRISM sample. For

each consumer we compute the percent change in the local house prices, ∆HP index from

December 2006-December 2009, using the CoreLogic Solutions single family combined house

price index (ZIP code level if available, and otherwise county-level); we do this regardless of

the consumers housing status. The average change is a drop of 18.4% for CCP and a drop

of 20.4% for CRISM borrowers. We also compute the change in county unemployment rates

from the Bureau of Labor Statistics from December 2006 to December 2009, ∆U and this

averages a 5.5 percentage point increase. Both of these variables display a large degree of

dispersion – in CCP the 5-95 percentile range is from -47.2% to 2.2% for ∆HP and from

2.9% to 8.7% for ∆U. We also use a ZIP code level measure of auto sales for 2006, ZIP Con-

trol, which we compute by aggregating the individual-level auto loan origination variable.

This is meant to capture permanent geographical differences in auto sales, holding other

things constant, for example those between Manhattan and Los Angeles which have similar

characteristics in many dimensions accept for prevalence of car ownership. The units of this

14See Federal Reserve Board (2016). Furthermore, the new car share is pro-cyclical, which would tend toheighten the cyclical behavior of their measure.

15Another shortcoming of our measure is that it is a binary variable and we do not know the value of theauto purchased. However this is also similar to the variable used by Mian, Rao, and Sufi (2013) who startby car registrations, which do not have any value attached to them, and then aggregate to a dollar valueusing an aggregate auto sales measure produced by the Census Bureau. See Section 2.5 for more details.

14

Table 2: Summary Statistics

(a) CCP

Mean Std. Dev. Min 5% Median 95% Max

Originate × 100 8.56 27.98 0 0 0 100 100∆HP -18.4 15.1 -64.3 -47.2 -15.7 2.2 25.9∆U 5.50 1.86 0.0 2.9 5.3 8.7 14.0ZIP Control 7.15 4.49 0 2.59 6.33 13.82 95.76Bank Health 0.62 0.14 0 0.38 0.64 0.78 1.22

(b) CRISM

Mean Std. Dev. Min 5% Median 95% Max

Originate × 100 13.90 34.60 0 0 0 100 100∆HP -20.4 15.0 -64.3 -48.4 -17.7 0.8 25.9∆U 5.54 1.83 -0.4 3.0 5.4 8.7 15.0ZIP Control 7.95 5.34 0 3.22 6.80 15.88 95.76Bank Health 0.64 0.12 0 0.41 0.65 0.78 1.22

Notes: Originate is a variable that can take the values 0 or 1. The statistics in this table are for Originate × 100 for more

detail. ∆HP and ∆U are the changes in house prices and unemployment rate, respectively, expressed in percentage points.

Bank Health shows the fraction of the syndication portfolio of the banks in a county, in which Lehman Brothers had a lead

role for the banks in a county and it is in percentage points. ZIP control shows the per-capita sales of cars in 2006 based on

the origination variable, expressed in thousands of dollars.

variable is in $1,000 per capita and it averages $7,150 in CCP and $7,950 in CRISM.

Our final aggregate variable, Bank Health, is a county-level version of a key indicator

of bank health as provided by Chodorow-Reich (2014). We start by the bank-level measure

of fraction of the syndication portfolio where Lehman Brothers had a lead role. Next, we

collect information on how many branches/affiliates each bank is located in each of the U.S.

counties.16 The final step is to distribute the national value of the banks to counties by using

the share of branches each bank has in the county.17 The resulting variable will be such that

a higher values indicates worse bank health and will capture a decline in availability of credit

in the county.

In some of our analysis, we address the endogeneity of house prices using two instruments.

16Lists of branches and their addresses are from the Federal Financial Institutions Examination Council’s(FFIEC) and the banks’ websites. ZIP codes of banks’ addresses are then matched with the county namesusing the FIPS county-code sheet from US Census. When a ZIP code is shared by two or more counties, wemanually look up that branch’s address in Google Maps to determine which county it belongs to.

17For example, if there are two banks in a county with national bank health values of X1 and X2 and B1

and B2 branches in a county, then the county’s bank health will be (B1X1 + B2X2)/(B1 + B2).

15

Table 3: ZIP Code-Level Analysis

Dependent variable: Change in Consumption (2006 to 2009)

Mian Rao and Sufi (2013) Our Measure

Change in HP 0.018 0.004(2006 to 2009) (0.0010) (0.0004)

R2 0.153 0.025

Observations 6,263 6,220

Notes: The dependent variables in the regressions are computed using auto registrations for Mian, Rao, and

Sufi (2013) and car loan originations for our measure. Observations are at the ZIP code level, aggregated

using the method in MRS. Regressions include a constant which is not reported. All regressions are weighted

by the number of households in the ZIP code. Robust standard errors are reported in parenthesis.

Both of these instruments capture the elasticity of housing supply, and therefore the response

of house prices to demand shocks. First is the share of land in the borrower’s MSA that is

unavailable for real estate development, from Saiz (2010), which reflects physical constraints

governing land development. In addition, we use the MSA-level Wharton Residential Land

Use Regulation Index (WRLURI) from Gyourko, Saiz, and Summers (2008). The WRLURI

is a summary measure of the stringency of the local regulatory environment in each MSA,

based both on local and state-level factors, with higher levels reflecting greater stringency.

We also construct a Bartik-style instrument for the change in county-level unemployment

rates from 2006-2009, along the lines of Keys, Tobacman, and Wang (2014), by using the

interaction of the 2003 industry mix of employment in that local labor market and the

national change in industry employment (exclusive of the given county) from 2006-2009.

These measures are constructed using the Quarterly Census of Employment and Wages

(QCEW) at the county level.

2.5 ZIP Code-Level Analysis

To obtain a ZIP code level dataset analogous to that of Mian, Rao, and Sufi (2013), we

take our CCP-based individual-level auto loan origination variable, and aggregate to the

ZIP code level. This gives us 6,224 observations that simply count the number of auto loan

originations in a ZIP code.18 Along the lines of Mian, Rao, and Sufi (2013), we then allocate

18We have 43 less ZIP codes relative to Mian, Rao, and Sufi (2013). The difference may be due to thefact that we do not need to restrict to ZIP codes represented in the Polk data and we also use a more recentrelease of the CoreLogic Solutions house price index which affects the availability of the house price index

16

annual national retail auto sales (from the Census Bureau) across ZIP codes in proportion

to their share of auto loan originations in our data; for example, if a ZIP code in our dataset

accounted for 5% of all auto loan originations for that year, it would be allocated 5% of

national retail auto sales. In contrast to Mian, Rao, and Sufi (2013), we use total auto sales,

both new and used, since our loan origination data does not distinguish between the two

(and, as we have argued above, this is more appropriate when studying consumer spending).

We then divide by the number of households in the ZIP code, which we obtain by applying

the national population growth rate to the ZIP code populations in the 2000 Census. Note

that the ZIP Code control variable we referred to in Section 2.1 is the 2006 version of this

variable.

Table 3 shows our replication of the results reported in column (5) in Table V of Mian,

Rao, and Sufi (2013). This is a simple OLS regression with change in consumption between

2006 and 2009 as the dependent variable and change in house prices in the same period

as the independent variable. Their estimate shows an $18 decline in auto consumption for

every $1,000 decline in house values. It is highly significant at 1% level. Our results shows a

smaller elasticity, $4 for every $1,000, which is also highly significant. This is reasonable due

to the exclusion of used car purchases in the measure used by Mian, Rao, and Sufi (2013).

When a consumer chooses to buy a used car instead of a new car in 2009, this purchase

shows up in our dataset (and thus consumption in 2009 does not fall as much) while it does

not show up in the measure used by Mian, Rao, and Sufi (2013).

3 Empirical Strategy: Individual-Level Analysis

As we explained in the previous section, our key dependent variable, auto loan originations

for an individual in 2009, is a binary variable. We conduct our analysis by estimating various

linear probability models using ordinary least squares (OLS) or instrumental variables (IV).

Results are very similar if we use a probit model instead of a linear probability model.

The generic equation we estimate in either of our datasets is

yizc = α + λ1agei + λ2age2i +

K∑k=1

4∑j=1

βjkCkizcX

jcz + εizc (1)

where the subscripts i, z and c refer to an individual, a ZIP code and a county. The dependent

variable shows whether or not the individual originated an auto loan in 2009. We control

used in the analysis.

17

for any life-cycle effects by a quadratic polynomial in age.

In our full model, we have four other controls, each of which are interacted with a full set

of individual-level categorical variables. These controls, denoted by Xjcz, are ZIP code-level

house price change, ∆HP, county-level change in the unemployment rate, ∆U, the 2006 ZIP

code auto sales control ZIP Control and the county-level bank health variable Bank Health in

(1). We keep the age polynomial, ∆HP and ZIP Control in all regressions and in addition to

the full model consider specifications that exclude one or both of the remaining two control

variables. These regressions help us identify the key channels of the effect of house prices as

we explain shortly.

All four of our control variables are interacted by a full set of dummy variables obtained

from up to three individual-level categorical variables, which are denoted by Ckicz for k =

1, ..., K. In regressions using CCP we categorize individuals in two dimensions: whether or

not they are prime (two values) and their homeownership status (five values). Considering all

combinations, and dropping as necessary to avoid multicollinearity, we get K = 9 interaction

variables per control variable. In CRISM, on the other hand, we can have up to a three-way

interaction that includes prime status (two values), mortgage type (five values) and LTV

(three values), leading to K = 29 interaction variables per control variable.

In all our estimations we cluster standard errors at the ZIP code level. Since our es-

timations result in tens of coefficient estimates, we focus on one key number, the average

marginal impact of a unit change in ∆HP, and report it either in aggregate or for various

subcategories j. In OLS, naturally this amounts to the sum of the appropriate combinations

of βjk. We compute the standard error of these marginal impacts using the delta method.

We use three regressions in each of our datasets in order to identify the importance of

the three channels for explaining the effect of changes in house prices on consumption. We

start by using only ∆HP and ZIP Control as controls. We record the marginal impact for

∆HP and normalize this to 100. Next we add ∆U to the model and compute the marginal

impact for ∆HP in this model. Controlling for ∆U typically reduces the marginal impact for

∆HP and this decline relative to the marginal impact we obtained in the first regression is

our measure of the general equilibrium effect. Next we add Bank Health in to the regression

and compute the marginal impact for ∆HP – this is our full model. The difference between

this and the one we computed from the second regression is our measure of the effect of

the decline in bank credit supply to households. Finally, after using all the controls, what

remains in terms of the marginal impact of ∆HP is the combination of the household wealth

and the household credit constraints.

18

We identify the magnitude of the wealth effect using three segments of the population,

two using CCP and one using CRISM. These are (a) prime homeowners who did not move

between 2006 and 2009 and own their houses without a mortgage or “free and clear”; (b)

prime homeowners who did not move between 2006 and 2009 and hold a mortgage; (c) prime

homeowners who have a fixed-rate mortgage, no second mortgage and an LTV that is less

than 50%. Recall that for us to label it wealth effect, a consumer should react to a change

in house prices only because it reduces his wealth, and not because some constraints the

consumer faces either today or in the future become more binding, or because the change in

house prices are correlated with other aggregate things (such as unemployment risk) he cares

about. All three segments of the population we use for this purpose fit this broad definition.

First, because they are all prime, they are less sensitive to aggregate conditions we may not

be controlling for. Second, the free-and-clear group does not hold a mortgage and thus they

have no financial constraints that is directly related to house prices. Similarly, the second

and third groups are least likely to have binding financial constraints. The third group is

especially relevant since they are not worried about changing terms of their mortgage when

house prices change. Moreover with a low LTV, they are immune to adverse effects of large

changes in house prices – for example it would take a decline in house prices over 50% to

wipe out their equity in their house, which happened for only a small fraction of homeowners

in our sample.

The change in house prices and employment are endogenous. Note that, it is not plausible

to have individual level auto loan origination to effect ZIP code level house prices and

county level employment, and hence we do not worry about reverse causality, nevertheless

an omitted factor, both at the ZIP code and/or county level may drive our dependent and

independent variables simultaneously. This is why we instrument both house prices changes

and employment changes. We follow the literature to instrument house prices changes based

on elasticity of housing supply and for employment changes we construct a Bartik-type

instrument.

4 Results

This section presents our decomposition results, first with CCP and then with CRISM. We

then provide IV results .

19

Table 4: CCP Decomposition

Only ∆HP ∆HP and ∆U Full

Overall HP Effect 0.0353 (***) 0.0230 (***) 0.0141 (***)% of Only ∆ HP 100% 65% 40%

Categories

Prime 0.0275 (***) 0.0156 (***) 0.0034Non-Prime 0.0527 (***) 0.0390 (***) 0.0348 (***)

Renters 0.0267 (***) 0.0151 (***) 0.0073 (**)Free-and-Clear Homeowners 0.0544 (***) 0.0351 (***) 0.0225 (***)Non-Mover Homeowners with Mortgage 0.0220 (***) 0.0167 (***) 0.0102 (***)Moving Homeowners with Mortgage 0.0894 (***) 0.0694 (***) 0.0583 (***)Miscellaneous 0.0295 (***) 0.0217 (***) 0.0155 (***)

Prime Renters 0.0272 (***) 0.0145 (***) 0.0028Prime Free-and-Clear Homeowners 0.0397 (***) 0.0212 (***) 0.0034Prime Non-Mover Homeowners with Mortgage 0.0123 (***) 0.0096 (***) 0.0008

Number obs. 6,553,884 6,553,884 6,553,884

Notes: All regressions include age, age2 and as well as 2006 ZIP-code control interacted with a full set of dummies. (***), (**)

and (*) denote significance at 1%, 5% and 10% levels, respectively.

4.1 Decomposition of Channels using CCP

We start by estimating the model using CCP. As we discussed earlier, CCP is representative

of the U.S. population and as we now demonstrate it contains a large degree of heterogeneity.

Table 4 reports our results.19 Each column shows the estimated model, starting from the

one with only ∆HP and ZIP Control, then adding ∆U and Bank Health in the second and

third columns.

The first row shows the overall marginal impact of the change in house prices on consump-

tion. Before controlling for key aggregate variables, the marginal impact on consumption

is 0.0353 and it is highly significant. To put this number in perspective, since the average

change in house prices is −18.4, our results show that the probability of originating a car loan

goes down by about 0.65 percentage points. Considering that the unconditional probability

of originating an auto loan is 8.56%, this is a sizable response.

Controlling for ∆U reduces the effect of house prices by 35% and controlling for Bank

Health reduces it by a further 25%. These results constitute our first key result. Out of

19In all tables that follow we use (***), (**) and (*) to denote significance at 1%, 5% and 10% levels,respectively. Moreover unless otherwise specified, these tables will report the marginal impact of a unitchange in ∆HP in the aggregate or for some subgroups of individuals.

20

the overall response of consumption to house prices measured without controls (except for

ZIP-code sales in 2006), 35% of it is explained by general equilibrium effects and 25% of it

is explained by the decline in credit due to deteriorating bank health. The remaining 40%

is the direct effect of house prices on consumption – the leftmost arrow in Figure 1. Much

of the rest of the paper will be devoted to understanding and further decomposing this 40%

response.

In the rest of Table 4, we show how various subgroups in our sample are affected by the

change in house prices, and how this varies across different specifications. In CCP we have

two main categories: prime / nonprime and the five homeownership categories we defined

in Section 2. The third and fourth rows of Table 4 show the results for the prime and

non-prime groups and the next five rows show the results for each homeownership category.

Finally the next three rows show the results for prime individuals who belong to one of

the three key homeownership categories. Looking at the first column reveals the extent

of heterogeneity: non-prime consumers react almost twice as much as prime consumers;

homeowners with a mortgage who has moved respond four times as much as those that did

not move. Controlling for ∆U reduces the responses across the board but all groups show

highly statistically significant responses. When we also control for Bank Health in the last

column, some very interesting results emerge. First, prime consumers’ reaction to changes in

house prices become insignificant. Second, looking deeper, the reactions of prime consumers

in all three important homeownership categories become insignificant. This shows that the

strong responses for these groups that we found in the first column, were not actually due to

the decline in house prices but due to other aggregate developments (such as the increase in

unemployment or decline in bank health), which are related to but distinct from the decline

in house prices. Third, despite the decline in the overall response, there is still considerable

heterogeneity in responses: homeowners with a mortgage who has moved now respond over

five times as much as those that did not.

The results for the two homeowner groups allow us to disentangle the household wealth

effect and the effect of household financial constraints. Consumers that are prime and either

own their home without a mortgage or have a mortgage and have not moved, would be most

immune from any other effect of house price changes but the wealth effect. Thus the results

for these groups allow us to conclude that the wealth effect is negligible and all of the 40%

that we attributed to the wealth effect and the effect of household financial constraints is

indeed due to the latter.

21

Table 5: CRISM OLS Results

(a) Decomposition

Only ∆HP ∆ HP and ∆U Full

Overall HP Effect 0.0682 (***) 0.0436 (***) 0.0298 (***)% of Only ∆ HP 100% 64% 44%

Categories

Prime 0.0566 (***) 0.0351 (***) 0.0186 (***)Non-Prime 0.1020 (***) 0.0699 (***) 0.0647 (***)

LTV0 0.0540 (***) 0.0240 (***) 0.0097 (*)LTV1 0.0755 (***) 0.0574 (***) 0.0457 (***)LTV2 0.1071 (***) 0.0920 (***) 0.0764 (***)

Prime LTV0 0.0435 (***) 0.0143 (**) -0.0037

Number obs. 677,918 677,918 677,918

Notes: (***), (**) and (*) denote significance at 1%, 5% and 10% levels, respectively.

(b) Implied Change in Probability of Origination Based on Average Value inVariable (in p.p.)

Only ∆HP ∆HP and ∆U Full

House Price -1.39 -0.81 -0.61Unemployment - -1.72 -1.83Bank Health - - -3.64

4.2 Decomposition of Channels using CRISM

In this section we confirm that the results regarding the decomposition of the house price

response also hold using CRISM. We do so using OLS like we did with CCP in the previous

section, as well as an IV specification that accounts of the endogeneity in house price and

unemployment changes.

The first panel of Table 5 shows the results from CRISM analogous to Table 4. To

keep things simple we only report results for the prime / nonprime groups, the three LTV

groups and the prime LTV0 group. The first two rows show that the overall results are

larger than those for CCP, but remarkably similar in therm of the percentage changes across

22

columns reported in the second row. Controlling for the change in unemployment reduces

the house price response by 36% and further controlling for bank health reduces the response

by another 20%.

Looking at the rest of the first panel of Table 5, we see that the largest effect of controlling

for the change in unemployment and bank health was on prime consumers, whose response

goes down by almost 70%. This causes a difference that is 3.5 times between prime and

non-prime consumers. As we further show in the next section, the non-prime response itself

is very heterogenous. LTV is also an important determinant of their consumption response:

those with low LTV have a negligible response while those with higher LTVs show sizable

responses; those with LTV greater than 80% respond over 2.5 times the average response.

Finally, in CRISM, we argue we can identify the wealth effect by prime consumers whose

LTV is less than 50%. This group does not show a statistically significant response once we

properly include the controls. Thus we again conclude that the wealth effect is negligible.

Our discussion so far has focused on the effect of the house price changes on consumption

and how controlling for the change in the unemployment rate or bank health is crucial for

properly measuring this. However, it is important to emphasize that these two variables

actually do more than just absorbing some of the effect of house prices on consumption.

The second panel of Table 5 show how each of the three variables contribute to explaining

the probability of originating an auto loan. To ease interpretation, we report the change

in origination probability due to each variable, which multiplies the marginal effect of each

variable with its average of value. For reference, recall that the unconditional probability of

originating an auto loan in the CRISM sample is 13.9%. Without other controls, ∆HP would

create a decline of 1.39 percentage points, which, at 10% of the unconditional probability, is

really large. Once other controls are introduced, this falls down to 0.61 percentage points.

In contrast, the average change in unemployment reduces the origination probability by

1.83 percentage points and the average bank health reduces it by 3.64 percentage points.

This shows that the two aggregate controls have very important independent effects on

consumption.

We also conduct an IV estimation using CRISM in order to take into account endogeneity

and omitted variable problems. We introduced the three instruments we use in Section 2.

Since our full specification includes interactions of what we consider to be endogenous vari-

ables in this IV (∆HP and ∆U) and individual level dummy variables, instead of estimating

the model using the full sample, we estimate separate IV models for each subsample. This

is equivalent to, and simpler than, running a single IV regression. We do this using six

23

Table 6: CRISM IV Results

(a) Sample First Stage (For Full Model, LTV0, Prime)

∆HP ∆U

WRLURI -0.0089 (***) -0.2887 (***)Unavailable -0.2345 (***) 3.1699 (***)Bartik 1.3103 (***) -23.6776 (***)ZIP Code Control -0.0048 (***) 0.0233 (***)Bank Health -22.8514 (***) 162.3166 (***)N 251,169 251,169R2 0.21 0.19F-stat 252.05 302.2

Notes: A constant and estimates for age and age2 are omitted from the table. (***), (**) and (*) denote significance at 1%,

5% and 10% levels, respectively.

(b) Marginal effect of ∆HP in various IV specifications.

Category HP Only HP and U Full

Prime LTV0 0.0924 (***) 0.0465 -0.0349Prime LTV1 0.1148 (***) 0.1262 (***) 0.0834 (**)Prime LTV2 0.1556 (***) 0.2242 (***) 0.1838 (***)Non-Prime LTV0 0.0914 (***) 0.0283 (***) 0.0022Non-Prime LTV1 0.1193 (***) 0.1266 (***) 0.1227 (**)Non-Prime LTV2 0.1616 (***) 0.1421 (*) 0.1028Overall 0.1087 (***) 0.0909 (***) 0.0379 (***)% of HP Only 100% 84% 35%

Notes: (***), (**) and (*) denote significance at 1%, 5% and 10% levels, respectively.

subsamples where we group consumers based on their prime status and LTV. Our results

are presented in Table 6. The first panel shows the first stages in one of the subgroups as an

example. All other subgroups have very similar first stage estimates both in terms of signs

and magnitudes. All first stages pass weak and under-identification tests.

The second panel shows the marginal effects for each subgroup for the three specifications

in terms of which controls are included. There are some significant differences relative to the

OLS results – IV results are typically larger. Focusing on the breakdown at the last row,

which is computed as the weighted average of the six subsample results, the importance of

∆U is smallerthan the OLS results at 16%, and the importance of the bank credit supply

24

Table 7: Decomposition of Channels

CCP CRISM - OLS CRISM - IV

Household Wealth 0% 0% 0%Household Financial Constraints 40% 44% 35%Bank Credit Supply to Households 25% 20% 49%General Equilibrium 35% 36% 16%

to households is larger at nearly 50%. The wealth effect is still negligible as shown by the

response of prime LTV0 borrowers.

Table 7 summarizes our results in terms of the importance of each channel across different

specifications and datasets.

4.3 Household Financial Constraints

Our results so far show that between 35% and 44% of the overall consumption response to

house price changes is driven by household financial constraints. In this section we investigate

further and attempt to identify which constraints are responsible for this large response.

We think of financial constraints in two broad categories: ex-ante and ex-post. By ex-

ante financial constraints we mean those that affected consumers in 2006 or earlier, before

house prices declined. Ex-post constraints are those that affect the consumers in 2009 and

they are likely tightened at least in part due to the decline in house prices. Our detailed

individual-level data allows us to cut the data various ways to identify these constraints.

4.3.1 Ex-Ante Constraints

We provide three ways of observing ex-ante financial constraints at work. First two are

shown in Table 8. Here, using CRISM we show how the interaction of LTV and prime

status affects the consumption response to ∆HP. This is just a more detailed breakdown of

the results in Table 5. There are three clear conclusions. First, being non-prime in 2006

significantly increases the consumption response in 2009. Second, having high LTV in 2006

also significantly increases the response. In fact, while the prime and LTV0 groups each show

no response to ∆HP, either being non-prime or having higher LTV matters a lot. Third,

not surprisingly, the interaction of the two creates a significant consumption response. Once

again after appropriate rescaling, the 0.1100 marginal effect we show correspond to a 1.5

25

Table 8: Ex-Ante Financial Constraints 1 - Prime Status and LTV

Prime Non-Prime Overall

LTV0 -0.0037 0.0495 (***) 0.0097LTV1 0.0371 (***) 0.0717 (***) 0.0457 (***)LTV2 0.0651 (***) 0.1100 (***) 0.0764 (***)Overall 0.0186 (***) 0.0647 (***) 0.0298 (***)

Notes: (***), (**) and (*) denote significance at 1%, 5% and 10% levels, respectively.

percentage point decline in the probability of auto loan origination.

We view both of these characteristics as being a sign of having constraints in 2006. Being

nonprime shows the presence of some adverse credit activity and this further influences the

type of credit the consumer gets access to. Moreover, non-prime status very persistent. Our

data shows that there is a 72% probability that a person who is non-prime in 2006 remains

non-prime in 2009. Being non-prime in 2009 has obvious adverse effects on access to credit

in 2009 and this limits how much consumption the consumer can have, especially using our

measure of auto loan originations. LTV in 2006 directly reflect the severity of one of the

most important financial constraints, the implicit collateral constraint of a mortgage. The

higher the LTV, the more constrained the consumer is and thus the more vulnerable he is to

house price changes. To sum up, both of these characteristics have implications about how

easy it is for the consumers to refinance their mortgage, how likely it is for them to default

and more generally how constrained they are.

The third way of identifying ex-ante constraints in our data is presented in Table 9. To

produce this table, we repeat our benchmark CRISM estimation but in addition to prime

status and LTV, we include a third layer of interaction with the mortgage type variable

defined in Section 2.1. To see how mortgage type is a sign of ex-ante constraints, note

that borrowers are not allocated randomly to different mortgage types, but they select the

mortgage that best suits their situation, including financial constraints they face. For ex-

ample, borrowers with closed-end second mortgages typically get these mortgages because

they lack the resources to make a 20% downpayment, which is the standard amount in most

mortgages. Further analyzing the distribution of consumers in Table 1 we see a few more

interesting patterns that suggest choices by consumers. For example short-maturity ARMs

seems to be chosen by prime low LTV borrowers (perhaps because they intend to pay off

their loan in a short period of time) or non-prime moderate-LTV borrowers (perhaps becu-

ase this was the only product they qualified for and they hope to refinance before the ARM

26

Table 9: Ex-Ante Financial Constraints 2 - Mortgage Type

(a) Marginal effects by category

Prime Non-PrimeLTV0 LTV1 LTV2 LTV0 LTV1 LTV2

Fixed Rate 0.0044 0.0432 (***) 0.0613 (***) 0.0523 (***) 0.0565 (***) 0.0826 (***)ARM < 5yr -0.0381 0.0492 0.108 (**) 0.0544 0.0781 (**) 0.0593ARM ≥ 5yr -0.077 (***) -0.0558 (*) 0.007 -0.0272 0.0899 0.0941CE Second 0.0485 (**) 0.0787 (***) 0.1063 (**) 0.092 (***) 0.0793 (**) 0.219 (***)HELOC -0.0177 0.0238 0.047 (**) 0.0196 0.0873 (***) 0.099 (**)

Notes: (***), (**) and (*) denote significance at 1%, 5% and 10% levels, respectively. Color coding in cells show the weight of

each cell in the overall CRISM population using the distribution reported in Table 1. Green represents a group with more than

10% weight, yellow shows a weight between 5% and 10% and purple shows a group that has a weight between 1% and 5%.

(b) Share of each category in overall effect

Prime Non-PrimeLTV0 LTV1 LTV2 LTV0 LTV1 LTV2 Total

Fixed Rate 4% 17% 9% 12% 11% 6% 58%ARM < 5yr -2% 2% 2% 2% 3% 1% 8%ARM ≥ 5yr -4% -3% 0% 0% 1% 1% -5%CE Second 5% 6% 3% 4% 4% 4% 27%HELOC -9% 6% 5% 2% 5% 3% 12%Total -6% 28% 19% 20% 24% 15% 100%

Notes: This table takes the marginal effect of a cell in panel (a), multiplies with the share of this cell in the population as

reported in Table 1 and divides by the overall effect. Red color denotes cells whose contribution is greater than 10%.

resets). HELOCs seem to be favored by prime borrowers with low-to-moderate LTVs. It is

plausible that these consumers use the extra liquidity from their HELOCs to finance some

consumption expenditures. Thus a decline in house prices would make their constraints bind

since banks can (and did) reduce HELOC limits of consumers with increased LTVs.20

The first panel of Table 9 shows the marginal impact of house price changes, broken down

into these three categories. To focus on the important results, we use colors to show the

weight of each subgroup in the whole population using the distribution in Table 1. Green

represents a group with more than 10% weight, yellow shows a weight between 5% and

20One may be tempted to think consumers can use cash they get from their HELOCs to finance an autopurchase completely without the need for an auto loan. If this was the case then it is not clear how we canidentify our results based on auto loan originations for people with HELOCs. Results reported by McCullyand Vine (2015) show that very few consumers purchase cars outright using HELOCs or cash-out refinancingusing data from three nationally representative surveys.

27

10% and purple shows a group that has a weight between 1% and 5%. We find that prime

borrowers with a fixed-rate mortgage and a low LTV, a group that represents over 23% of

the population, do not respond to changes in house prices. This is yet another indication

that wealth effect is not important as these consumers would have no financial constraints.

We find significant responses for the remainder of the fixed-rate group who have higher LTV

and/or are non-prime. There are negligible responses from consumers with short-maturity

ARMs. Prime consumers with long-maturity ARMs, on the other hand, respond strongly

and negatively to house price changes. This means they benefited from the decline in house

prices. Consumers with closed-end second mortgages seem to be the most responsive group

where those that are non-prime and have high LTV shows a response that is over 7 times the

average response. Finally consumers with HELOCs that also have moderate-to-high LTVs

show significant responses.

While the results in the preceding paragraph are interesting, they do not fully answer

our main goal of finding out what constraints are responsible for the large response of con-

sumption to house prices. To do so, we compute the contribution of each cell in the first

panel of Table 9 to the overall response. This amounts to taking the marginal effect of a

particular group, multiplying by the weight in population in Table 1 and dividing by the

overall response. Results are reported in the second panel of Table 9. To ease interpretation,

we highlight cells that show a contribution grater than 10%.

We find that almost 60% of the consumption response to changes in house prices come

from consumers with fixed-rate mortgages, especially those that are non-prime (29%) or are

prime and have moderate-to-high LTV (26%). The contribution of consumers with ARMs

is negligible. Consumers with second mortgages constitute about 40% of the response, with

those with closed-end second at 27%.

Our reading of these results are as follows. We think all of the non-prime response,

amounting to 59%, represents ex-ante constraints. Similarly, any mortgage choice other than

a fixed rate mortgage also shows the presence of ex-ante constraints, which is an additional

11%. Thus our results indicate that at least 70% of the house price response stripped of any

general equilibrium and bank credit effects (recall that this itself is 44% of the full response)

is due to ex-ante credit constraints.

4.3.2 Ex-Post Constraints

To demonstrate the importance of ex-post financial constraints, those that become more

binding due to the decline in house prices, we consider a simple analysis. We use the same

28

Table 10: Ex-Post Financial Constraints - 2009 Prime Status

2006 Prime 2006 Non-Prime Overall

LTV0 2.18 (***) 0.29 1.69 (***)LTV1 5.63 (***) 3.02 (***) 4.86 (***)LTV2 8.78 (***) 5.24 (***) 7.82 (***)Overall 4.24 (***) 1.82 (***) 3.53 (***)

Notes: The table reports the marginal effect of each characteristic given in a cell to the probability of becoming non-prime in

2009, expressed in percentage points. Unconditional probability of being non-prime in 2009 is 26%. (***), (**) and (*) denote

significance at 1%, 5% and 10% levels, respectively.

regression model we used in CRISM, where all controls are interacted by all the combinations

of 2006 prime status and LTV, to predict consumers’ 2009 prime status. The goal here is to

demonstrate the importance of ∆HP in converting borrowers to non-prime in 2009, which is

precisely how our concept of ex-post constraints work. Table 10 shows the results. Here to

ease interpretation all marginal effects are converted to change in the probability of becoming

non-prime. To put things in perspective, the unconditional probability of being non-prime

in 2009 is 26%. It is also helpful to note that even after controlling for the effects of the

control variables, being non-prime in 2006 increases the probability of being non-prime in

2009 by 15 percentage points.

Table 10 shows that the average decline in house prices lead to a 3.53 percentage point

increase in the probability of an average person to become non-prime in 2009. This is already

very sizable. When we look at those that were prime in 2006 and especially those that had

moderate-to-high LTV, the increase in probability is 1.5 to 2.5 times larger – as high as 8.78

percentage points for prime borrowers who had a high LTV in 2006. Borrowers that were

non-prime in 2006, on the other hand, are much less affected by the house price decline –

the average effect is about half of the overall effect. This is because the large persistence in

non-prime status we mentioned above.

Our results regarding prime borrowers in 2006 explain why in Table 9 about 40% of

the response to house prices come from prime borrowers. While some of them certainly

could have financial constraints that influence them in 2006, at least for some their reaction

to consumption is due to the simple reason that they have become non-prime due to the

decline in house prices. We think that this is likely due to the consumer falling behind

(either voluntarily or involuntarily) his mortgage payments. This, in turn, reduced the

creditworthiness of the consumer to the point where we label him non-prime. Crucially, it

also means that he is less likely to qualify for an auto loan and thus we observe that he

29

reduces his consumption.

It is important to emphasize that this result seems to also be relevant for the current

debate in the literature in terms of whether the boom period mortgage borrowing was dri-

ven by prime and sub-prime borrowers. Albanesi, De Giorgi, and Nosal (2017) show that

borrowing in sub-prime ZIP codes is driven by prime borrowers in those ZIP codes, whereas

borrowing by subprime individuals was constant in the boom period. Our results of prime

borrowers turning into non-prime due to a decline in house prices is consistent with these

findings given that prime borrowers did most of the borrowing during boom period, which

then fell behind in their payments.

5 Conclusion

We use individual level data to decompose the response of consumption to declining house

prices during 2006–2009. We find that wealth effect is not important for this response,

whereas financially constrained households and lower credit supply from banks who got hit

by the crisis explain most of the response. Our decomposition exercise is based on accounting

for the role of employment changes and bank health in a given county and identifying the

groups of individuals carefully so that persons exposed to wealth effects and a possible

tightening of financial constraints can be investigated separately.

In terms of our estimates, tightening household-level financial constraints can explain

40-45 percent of the response of consumption to declining house prices. Deteriorating bank

health leads to reduced credit supply to households which explains 20-25 percent of the

consumption response. The remaining 35 percent is a general equilibrium effect that works

via a decline in employment as a result of either lower credit supply to firms or the feedback

from lower consumer demand.

Using elasticity for housing supply and prior national sectoral employment growth as

instruments for changes in house prices and unemployment, we run IV regressions. Our

estimate of a negligible wealth effect is robust to accounting for the endogeneity of house

prices and unemployment. The contribution of tightening household financial constraints

goes down to 35 percent, whereas declining bank credit supply to households captures about

half of the overall consumption response, once we account for endogeneity.

30

References

Adelino, M., A. Schoar, and F. Severino (2017): “Dynamics of Housing Debt in

the Recent Boom and Great Recession,” Discussion paper, National Bureau of Economic

Research.

Aladangady, A. (2017): “Housing Wealth and Consumption: Evidence from

Geographically-linked Microdata,” American Economic Review, 107(11), 3415–46.

Albanesi, S., G. De Giorgi, and J. Nosal (2017): “Credit Growth and the Financial

Crisis: A New Narrative,” Discussion paper, National Bureau of Economic Research.

Attanasio, O. P., L. Blow, R. Hamilton, and A. Leicester (2009): “Booms and

Busts: Consumption, House Prices and Expectations,” Economica, 76(301), 20–50.

Bahaj, S. A., A. Foulis, and G. Pinter (2017): “Home Values and Firm Behaviour,” .

Barro, R. J. (1976): “The Loan Market, Collateral, and Rates of Interest,” Journal of

Money, Credit and Banking, 8(4), 439–456.

Berger, D., V. Guerrieri, G. Lorenzoni, and J. Vavra (2015): “House prices and

consumer spending,” The Review of Economic Studies.

Bernanke, B. S., M. Gertler, and S. Gilchrist (1999): “The Financial Accelerator in

a Quantitative Business Cycle Framework,” Handbook of macroeconomics, 1, 1341–1393.

Buiter, W. H. (2008): “Housing Wealth Isn’t Wealth,” Discussion paper, National Bureau

of Economic Research.

Calomiris, C., S. D. Longhofer, and W. Miles (2009): “The (Mythical?) Housing

Wealth Effect,” Discussion paper, National Bureau of Economic Research.

Calomiris, C. W., S. D. Longhofer, and W. Miles (2012): “The Housing Wealth

Effect: The Crucial Roles of Demographics, Wealth Distribution and Wealth Shares,”

Discussion paper, National Bureau of Economic Research.

Campbell, J. Y., and J. F. Cocco (2007): “How Do House Prices Affect Consumption?

Evidence from Micro Data,” Journal of Monetary Economics, 54(3), 591–621.

Carroll, C. D., and M. S. Kimball (1996): “On the Concavity of the Consumption

Function,” Econometrica, pp. 981–992.

31

Carroll, C. D., M. Otsuka, and J. Slacalek (2011): “How Large are Housing and

Financial Wealth Effects? A New Approach,” Journal of Money, Credit and Banking,

43(1), 55–79.

Case, K. E., J. M. Quigley, and R. J. Shiller (2005): “Comparing Wealth Effects:

the Stock Market versus the Housing Market,” Advances in Macroeconomics, 5(1).

Charles, K. K., E. Hurst, and M. J. Notowidigdo (forthcoming): “Housing Booms

and Busts, Labor Market Opportunities, and College Attendance,” .

Chen, B. S., S. G. Hanson, and J. C. Stein (2017): “The Decline of Big-bank Lending

to Small Business: Dynamic Impacts on Local Credit and Labor Markets,” Discussion

paper, National Bureau of Economic Research.

Chodorow-Reich, G. (2014): “The Employment Effects of Credit Market Disruptions:

Firm-level Evidence from the 2008–9 Financial Crisis,” The Quarterly Journal of Econo-

mics, 129(1), 1–59.

Decker, R. A. (2015): “Collateral Damage: Housing, Entrepreneurship, and Job Crea-

tion,” Discussion paper, Federal Reserve Board.

Demyanyk, Y. S., D. Hryshko, M. J. Luengo-Prado, and B. E. Sørensen (2015):

“The Rise and Fall of Consumption in the ’00s,” Discussion paper, Federal Reserve Bank

of Boston.

Duygan-Bump, B., A. Levkov, and J. Montoriol-Garriga (2015): “Financing Con-

straints and Unemployment: Evidence from the Great Recession,” Journal of Monetary

Economics, 75, 89–105.

Elul, R., N. S. Souleles, S. Chomsisengphet, D. Glennon, and R. Hunt (2010):

“What ”Triggers” Mortgage Default?,” American Economic Review, 100(2), 490–94.

Elul, R., and S. Tilson (2015): “Owner Occupancy Fraud and Mortgage Performance,”

Discussion paper, Federal Reserve Bank of Philadelphia.

Federal Reserve Board (2016): “Report on the Economic Well-Being of U.S. House-

holds in 2015,” Discussion paper.

Gilchrist, S., M. Siemer, and E. Zakrajek (2017): “Real Effects of Credit Booms and

Busts: A County-Level Analysis,” Discussion paper, Mimeo.

32

Greenstone, M., A. Mas, and H.-L. Nguyen (2015): “Do Credit Market Shocks affect

the Real Economy? Quasi-Experimental Evidence from the Great Recession and Normal

Economic Times,” .

Gropp, R., J. Krainer, and E. Laderman (2014): “Did Consumers Want Less Debt?

Consumer Credit Demand versus Supply in the Wake of the 2008-2009 Financial Crisis,”

.

Guerrieri, L., and M. Iacoviello (2017): “Collateral Constraints and Macroeconomic

Asymmetries,” Journal of Monetary Economics, 90, 28–49.

Gyourko, J., A. Saiz, and A. Summers (2008): “A New Measure of the Local Regulatory

Environment for Housing Markets: The Wharton Residential Land Use Regulatory Index,”

Urban Studies, 45(3), 693–729.

Hart, O., and J. Moore (1994): “A Theory of Debt Based on the Inalienability of Human

Capital,” The Quarterly Journal of Economics, 109(4), 841–879.

Iacoviello, M. (2005): “House Prices, Borrowing Constraints, and Monetary Policy in

the Business Cycle,” American Economic Review, 95(3), 739–764.

Johnson, K., K. Pence, and D. Vine (2017): “Auto Sales and Credit Supply,” Discussion

paper, University of Pennsylvania.

Justiniano, A., G. E. Primiceri, and A. Tambalotti (2017): “Credit Supply and the

Housing Boom,” Discussion paper.

Kaplan, G., K. Mitman, and G. L. Violante (2016): “Non-durable Consumption

and Housing Net Worth in the Great Recession: Evidence from Easily Accessible Data,”

Discussion paper, National Bureau of Economic Research.

(2017): “The Housing Boom and Bust: Model Meets Evidence,” Discussion paper,

National Bureau of Economic Research.

Keys, B. J., J. Tobacman, and J. Wang (2014): “Rainy Day Credit? Unsecured Credit

and Local Employment Shocks,” Discussion paper, Federal Reserve Board, Finance and

Economics Discussion Series, 2014-82.

Kiyotaki, N., and J. Moore (1997): “Credit Cycles,” Journal of Political Economy,

105(2), 211–248.

33

McCully, Brett A., P. K. M., and D. J. Vine (2015): “How Much Are Car Purchases

Driven by Home Equity Withdrawal? Evidence from Household Surveys,” Discussion

paper, Federal Reserve Board, Finance and Economics Discussion Series, 2015-106.

Mian, A., K. Rao, and A. Sufi (2013): “Household Balance Sheets, Consumption, and

the Economic Slump,” The Quarterly Journal of Economics, pp. 1687–1726.

Mian, A., and A. Sufi (2009): “The Consequences of Mortgage Credit Expansion: Evi-

dence from the US Mortgage Default Crisis,” The Quarterly Journal of Economics, 124(4),

1449–1496.

(2011): “House Prices, Home Equity–based Borrowing, and the US Household

Leverage Crisis,” The American Economic Review, 101(5), 2132–2156.

Philippon, T., and V. Midrigan (2016): “Household Leverage and the Recession,” .

Saiz, A. (2010): “The Geographic Determinants of Housing Supply,” The Quarterly Journal

of Economics, 125(3), 1253–1296.

Sinai, T., and N. S. Souleles (2005): “Owner-occupied Housing as a Hedge against Rent

Risk,” The Quarterly Journal of Economics, 120(2), 763–789.

Stiglitz, J. E., and A. Weiss (1981): “Credit Rationing in Markets with Imperfect

Information,” The American Economic Review, 71(3), 393–410.

34